code: def clone_conv_layers(base_model, cfg):
      feature_node_name = cfg["MODEL"].FEATURE_NODE_NAME
      start_train_conv_node_name = cfg[
          "MODEL"
      ].START_TRAIN_CONV_NODE_NAME
      last_conv_node_name = cfg["MODEL"].LAST_CONV_NODE_NAME
      if not cfg.TRAIN_CONV_LAYERS:
          conv_layers = clone_model(
              base_model,
              [feature_node_name],
              [last_conv_node_name],
              CloneMethod.freeze,
          )
      elif feature_node_name == start_train_conv_node_name:
          conv_layers = clone_model(
              base_model,
              [feature_node_name],
              [last_conv_node_name],
              CloneMethod.clone,
          )
      else:
          fixed_conv_layers = clone_model(
              base_model,
              [feature_node_name],
              [start_train_conv_node_name],
              CloneMethod.freeze,
          )
          train_conv_layers = clone_model(
              base_model,
              [start_train_conv_node_name],
              [last_conv_node_name],
              CloneMethod.clone,
          )
          conv_layers = Sequential(
              [fixed_conv_layers, train_conv_layers]
          )
      return conv_layers
  
imports: from __future__ import print_function
  import numpy
  import os, sys
  import argparse
  import easydict
  import cntk
  from cntk import Trainer, load_model, Axis, input_variable, parameter, times, combine, softmax, roipooling, plus, element_times, CloneMethod, alias, Communicator, reduce_sum
  from cntk.core import Value
  from cntk.initializer import normal
  from cntk.layers import placeholder, Constant, Sequential
  from cntk.learners import momentum_sgd, learning_parameter_schedule_per_sample, momentum_schedule
  from cntk.logging import log_number_of_parameters, ProgressPrinter
  from cntk.logging.graph import find_by_name, plot
  from cntk.losses import cross_entropy_with_softmax
  from cntk.metrics import classification_error
  from _cntk_py import force_deterministic_algorithms
  from utils.rpn.cntk_smoothL1_loss import SmoothL1Loss
  from utils.annotations.annotations_helper import parse_class_map_file
  from utils.od_mb_source import ObjectDetectionMinibatchSource
  from utils.proposal_helpers import ProposalProvider
label_desc: Use image node_id 4 for calling a global function with example usage: clone_conv_layers(base_model, cfg) and returns: conv_layers